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Hypoxia compromises the mitochondrial metabolism of Alzheimer’s disease microglia via HIF1

Nature Aging volume 1, pages 385–399 (2021)Cite this article

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Abstract

Genetic Alzheimer’s disease (AD) risk factors associate with reduced defensive amyloid β plaque-associated microglia (AβAM), but the contribution of modifiable AD risk factors to microglial dysfunction is unknown. In AD mouse models, we observe concomitant activation of the hypoxia-inducible factor 1 (HIF1) pathway and transcription of mitochondrial-related genes in AβAM, and elongation of mitochondria, a cellular response to maintain aerobic respiration under low nutrient and oxygen conditions. Overactivation of HIF1 induces microglial quiescence in cellulo, with lower mitochondrial respiration and proliferation. In vivo, overstabilization of HIF1, either genetically or by exposure to systemic hypoxia, reduces AβAM clustering and proliferation and increases Aβ neuropathology. In the human AD hippocampus, upregulation of HIF1α and HIF1 target genes correlates with reduced Aβ plaque microglial coverage and an increase of Aβ plaque-associated neuropathology. Thus, hypoxia (a modifiable AD risk factor) hijacks microglial mitochondrial metabolism and converges with genetic susceptibility to cause AD microglial dysfunction.

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Fig. 1: HIF1-mediated transcription is activated in AβAM.
Fig. 2: AD microglia increase aerobic respiration-related transcription.
Fig. 3: Mitochondria are elongated in an AD mouse model.
Fig. 4: Hypoxia induces cell cycle arrest via HIF1 in microglia in cellulo.
Fig. 5: Overstabilization of HIF1 reduces AβAM in vivo.
Fig. 6: Systemic sustained hypoxia decreases clustering of AβAM.
Fig. 7: Systemic sustained hypoxia enhances Aβ aggregation, spreading and Aβ plaque-associated axonal dystrophy.
Fig. 8: A human hypoxia-prone brain area contains nude Aβ plaques with increased local axonal dystrophy.

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Data availability

Transcriptomics data are available from the Gene Expression Omnubus with the following accession numbers: GSE97423 (mouse primary microglial cultures exposed to normoxia or hypoxia); GSE129296 (isolated Clec7a+ microglia from wild-type, APP and TAU mice); and GSE168059 (isolated microglia from APP-PSEN1/+; VHLfl/– with or without tamoxifen treatment). Source data are provided with this paper.

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Acknowledgements

We thank P. J. Ratcliffe for hosting A.H.-G. in his laboratory for the conduction of PhD experiments and L. del Peso for assistance with transcription factor enrichment analysis. We also thank K. Levitsky (microscopy), M. J. Castro (flow cytometry), F. J. Moron (genomics), E. Andres-Leon (bioinformatics), and R. Duran (histology) for advice and technical assistance in relation to experiments performed at the IBiS core facilities. R.M.-D. was the recipient of a Sara Borrell fellowship from Instituto de Salud Carlos III (ISCIII) (CD09/0007). N.L.-U., C.O.-d.S.L., C.R.-M. and M.I.A.-V. were the recipients of FPU fellowships from Spanish Ministry of Education, Culture and Sport (FPU14/02115, AP2010‐1598, FPU16/02050 and FPU15/02898, respectively). A.H.-G. was the recipient of an FPI fellowship from the Spanish Ministry of Education, Culture and Sport (BES-2010-033886). This work was supported by grants from the Spanish MINEICO, ISCIII and FEDER (European Union) (SAF2012‐33816, SAF2015‐64111‐R, SAF2017-90794-REDT and PIE13/0004 to A.P.); by the Regional Government of Andalusia co-funded by CEC and FEDER funds (European Union) (‘Proyectos de Excelencia’; P12‐CTS‐2138 and P12‐CTS‐2232 to A.P.); by the ‘Ayuda de Biomedicina 2018’, Fundación Domingo Martínez (to A.P.) ; by the ISCIII of Spain, co-financed by FEDER funds (European Union) through grants PI18/01556 (to J.V.) and PI18/01557 (to A. Gutierrez); by Junta de Andalucía, co-financed by FEDER funds (grants UMA18-FEDERJA-211 (to A. Gutierrez) and US‐1262734 (to J.V.)); and by Spanish MINEICO (BFU2016-76872-R and BES-2011-047721 to E.B.).

Author information

Author notes

  1. Antonio Heras-Garvin

    Present address: Division of Neurobiology, Department of Neurology, Medical University of Innsbruck, Innsbruck, Austria

  2. Clara Ortega-de San Luis

    Present address: School of Biochemistry and Immunology, Trinity College Institute for Neuroscience, Trinity College Dublin, Dublin, Ireland

  3. Manuel A. Sanchez-Garcia

    Present address: Medical Research Council Centre for Inflammation Research, The Queen’s Medical Research Institute, The University of Edinburgh, Edinburgh, UK

  4. Adrian Viehweger

    Present address: Department of Pediatric Radiology, University Clinic Leipzig, Leipzig, Germany

  5. These authors contributed equally: Rosana March-Diaz, Nieves Lara-Ureña, Carmen Romero-Molina, Antonio Heras-Garvin, Clara Ortega-de San Luis.

  6. These authors jointly supervised this work: Javier Vitorica, Alberto Pascual.

Authors and Affiliations

  1. Instituto de Biomedicina de Sevilla (IBiS), Hospital Universitario Virgen del Rocio/CSIC/Universidad de Sevilla, Seville, Spain

    Rosana March-Diaz, Nieves Lara-Ureña, Carmen Romero-Molina, Antonio Heras-Garvin, Clara Ortega-de San Luis, Maria I. Alvarez-Vergara, Manuel A. Sanchez-Garcia, Alicia E. Rosales-Nieves, Cristina Forja, Victoria Navarro, Maria V. Sanchez-Mico, Adrian Viehweger, Marisa Vizuete, Jose Lopez-Barneo, Javier Vitorica & Alberto Pascual

  2. Centro de Investigación Biomédica en Red sobre Enfermedades Neurodegenerativas (CIBERNED), Madrid, Spain

    Carmen Romero-Molina, Elisabeth Sanchez-Mejias, Jose C. Davila, Victoria Navarro, Angela Gomez-Arboledas, Maria V. Sanchez-Mico, Marisa Vizuete, Jose Lopez-Barneo, Antonia Gutierrez & Javier Vitorica

  3. Departamento de Bioquimica y Biologia Molecular, Facultad de Farmacia, Universidad de Sevilla, Seville, Spain

    Carmen Romero-Molina, Victoria Navarro, Maria V. Sanchez-Mico, Marisa Vizuete & Javier Vitorica

  4. Departamento Biologia Celular, Genetica y Fisiologia, Facultad de Ciencias, Universidad de Málaga, Malaga, Spain

    Elisabeth Sanchez-Mejias, Jose C. Davila, Angela Gomez-Arboledas & Antonia Gutierrez

  5. Instituto de Investigación Biomédica de Málaga (IBIMA), Universidad de Málaga, Málaga, Spain

    Elisabeth Sanchez-Mejias, Jose C. Davila, Angela Gomez-Arboledas & Antonia Gutierrez

  6. CIC bioGUNE, Basque Research and Technology Alliance (BRTA), Derio, Spain

    Almudena Gerpe & Edurne Berra

  7. Target Discovery Institute, Oxford, UK

    Emma J. Hodson & Tammie Bishop

  8. Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA

    Alberto Serrano-Pozo

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Contributions

A.P., J.V., R.M.-D., N.L.-U., C.R.-M., A.H.-G. and C.O.-d.S.L. conceived of and designed the research. A.P., R.M.-D., N.L.-U., C.R.-M., A.H.-G., C.O.-d.S.L., M.I.A.-V., M.A.S.-G., E.S.-M., J.C.D., A.E.R.-N., V.N., A.G.-A., M.V.S.-M., A.V. and A. Gerpe performed the research. R.M.-D., N.L.-U., C.R.-M., A.H.-G., C.O.-d.S.L., M.I.A.-V., M.A.S.-G., E.S.-M., J.C.D., A.E.R.-N., C.F., V.N., A.G.-A., M.V.S.-M., A.V., A. Gutierrez, M.V., T.B., A.S.-P., J.L.-B., E.B., J.V. and A.P. analyzed the data. E.J.H. and T.B. provided methodological and/or scientific assistance. E.J.H. and T.B. contributed mouse models/samples. A.P., E.B., J.V. and A.S.-P. wrote the manuscript.

Corresponding authors

Correspondence to Javier Vitorica or Alberto Pascual.

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Competing interests

The authors declare no competing interests.

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Peer review information: Nature Aging thanks Luca Scorrano and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Extended data

Extended Data Fig. 1 HIF1-mediated transcription in DAM.

a, Immunocytochemistry of primary cultures of microglia (left panel) and astrocytes (right panel) using antibodies against IBA1 (red) and GFAP (green). Scale bar is 20 µm. b, Relative levels of Cd33 and Gfap mRNAs estimated by qRT-PCR (a.u. arbitrary units) in microglial (M) and astrocyte cultures (A). Hmbs levels were used as housekeeping control (n is indicated between brackets, independent cultures, Student’s t-test, two-sided). c, Principal component analysis showing the separation between biological replicates of mouse primary microglial cell cultures exposed to normoxia (N: 21% O2, 6 h) or hypoxia (H: 1% O2, 6 h). d, Primary microglial cell cultures were exposed to N or H (24 h) and the relative levels of several mRNAs included in the HIF1/hypoxia-induced microglial module (HMM) were estimated by qRT-PCR. Hmbs levels were used as housekeeping control (n is indicated between brackets, biological independent cultures, Student’s t-test, two-sided). e–h, Gene set enrichment analysis (GSEA). e, GSEA of APP751SL/+ (left and middle right) or MAPTp301S/+ end-stage (ES) (middle left and right) DAM versus (vs) wild-type (WT) 12-month-old (mo) microglia. Enrichment plots of the HMM (left) and MGnD (right) GSs. The table contains the 15 top GSs with a FWER-p-value less than 0.05. f, GSEA of APP751SL/+ versus (vs) MAPTp301S/+ end-stage (ES). g, h, GSEA 5xfAD/+ DAM vs wild-type (WT) microglia (g, upper row MGnD GS; h, HMM GS); SOD1p.G93A/+ DAM vs WT microglia (g, left in the lower row); 24-mo vs 5-mo WT microglia (g, right in the lower row). Data are represented as mean ± S.E.M.

Source data

Extended Data Fig. 2 Gating strategy to isolate CLEC7a+ microglia.

Gate identification was performed according to guidelines and previous reports in contour density plots. a, Debris, and dead cells were discarded by forward (FSC) and side (SSC) scatters dispersion of events. b, Singlets of events were selected according to FSC height (FSC-H) versus area (FSC-A). c, Microglial cells, reactive for CD45 and CD11b markers, were selected (black box). d, e, The gate selected to isolate CLEC7a+ microglia is shown in (d) and was defined by performing the same experiment without CLEC7a antibody (e). f, Flow cytometry contour density plots of CD11b/CD45 reactive microglia with low (CLEC7a–) and high (CLEC7a+) levels. Right graph represents the percentage of Clec7a+ microglia in each experimental group; wild-type (WT), APP751SL/+ (APP/+) and MAPTp301S/+ (TAU/+); (n is indicated between brackets, biological independent experiments, ANOVA, Tukey’s test). g, Principal component analysis (left panels) and volcano plots (right panels) of the microarray studies presented in Figs. 1 and 2. Data are represented as mean ± S.E.M.

Source data

Extended Data Fig. 3 Mitochondrial activation in DAM.

a, GSEA of APP751SL/+ (left) or MAPTp301S/+ ES DAM (middle and right) versus (vs) wild-type (WT) 12mo microglia. Enrichment plots and heat maps (middle) of top 30 ranking leading edge genes of the oxidative phosphorylation (OXPHOS) GS. Red symbolizes overexpression and blue down regulation (see Supplementary Data Table for shade values). b, Sector graphs of the most enriched biological functions between the 15 top GSs enriched in APP751SL/+ and MAPTp.P301S/+ DAM vs WT microglia. c, GSEA of 5xfAD/+; SOD1p.G93A/+, and aged (24mo) DAM vs WT age matched or young (5mo) microglia. Enrichment plots and heat maps of up to the top 30 ranking leading edge genes of OXPHOS GS. d, GSEA of APP751SL/+ DAM vs wild-type (WT) 12mo microglia. Enrichment plot of the oxidative phosphorylation (OXPHOS) GS.

Extended Data Fig. 4 Gene sets enriched in hypoxic primary microglial cell cultures.

Enrichment plots of the GSs upregulated in hypoxic (1% O2; 6 h) versus normoxic (1% O2) primary microglial cell cultures. a, KEGG_GLYCOLYSIS_GLUCONEOGENESIS GS. b, KEGG_OXIDATIVE_PHOSPHORYLATION GS. c, d, Only significantly overrepresented GSs from biological processes (c) or KEGG (d) categories are displayed (FDR q-value < 0.05).

Extended Data Fig. 5 Gene sets downregulated in hypoxic primary microglial cell cultures.

Enrichment plots of the GSs downregulated in hypoxic (1% O2; 6h) versus normoxic (1% O2) primary microglial cell cultures. a, b, Only significantly overrepresented GSs from biological processes (a) or KEGG (b) categories are displayed (FDR q-value < 0.05).

Extended Data Fig. 6 Hypoxic cell cycle arrest in BV2 cells.

a, b, d, Cell cycle analysis using propidium iodide (PI) staining of BV2 cells exposed to normoxia (N: 21% O2, 48 h, a, left graph), hypoxia (H: 1% O2, 48 h, a, right graph), reoxygenation (R: 24 h H and 24 h N, b), or DMOG (D; 0.1 mM 24 h). The quantification of the percentage of cells found in the different cell cycle phases is shown in (b) and (d) (n = 3 biological independent cultures). c, BV2 cells were treated with a scrambled siRNA (siControl) or siRNAs against Hif1a (siHIF1a), Epas1 (siEpas1) and HIF1a-Epas1 (siHIF1a/Epas1). Interfered cells were exposed to N, H (24 h), or D (24 h) and the mRNA levels of Hif1a (left graph) and Epas1 (right graph), and the cell cycle (lower row) were estimated by qRT-PCR (n is indicated between brackets, biological independent cultures). Data are represented as mean ± S.E.M.

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Extended Data Fig. 7 Systemic sustained hypoxia does not alter Aß plaque-associated astrocytes or wild-type microglia.

a, Relative levels of Iba1 and Gfap mRNA extracted from the hippocampus of 14-month-old normoxic (N, 21% O2) and hypoxic (H, 9% O2) mice. Gapdh mRNA was used as housekeeping control (Student’s t-test, two-sided). b, Cortical Aß dense-core plaques stained with Thioflavin-S (Thio-S; green) surrounded by GFAP + reactive astrocytes (red) in 14-month-old APP-PSEN1/+ mice exposed to either N or H. Scales bar are 20 µm. Graphs show the quantification of the ratio of GFAP + astrocytes far (> 20 µm) per total astrocytes (Glutamine synthetase –GluS– immunoreactive astrocytes; left graph) and the ratio of GFAP+ astrocytes close (≤ 20 µm) per total astrocytes (GluS+; right graph) (ANOVA, Tukey’s test). c, Confocal projection of coronal brain slices from 8-month-old APP-PSEN1/+ mice injected with Evans blue (EB; white) and stained with an astrocytic (GFAP; green) and a nuclear (DAPI; blue) marker. Red arrowheads indicate reactive astrocytic endfeet. Scale bars are 20 µm. d, Hematocrit levels in N or H (Oxygen/Genotype: F = 0.63, p = 0.336; Oxygen: F = 283.06, p = 0.000; Univariate analysis of variance, non-adjusted for multiple correlations). e, Prussian blue staining of N or H. Scales bar are 100 µm. f, Quantification of the density of Ki67+ microglial cells in N or H. (Student’s t-test, two-sided). Data are represented as mean ± S.E.M. n is indicated between brackets, biological independent experiments.

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Extended Data Fig. 8 Systemic sustained hypoxia does not induce Aß deposition in wild-type mice.

a–c, 14-month-old wild-type mice were exposed to normoxia (N, 21% O2) or sustained hypoxia (H, 9% O2) for 21 days. No Thio-S (a), Aß (b) or pTAU (c) reactive deposits were found (n = 3 –N– or 4 –H– biological independent mice). Scale bars are 1 mm.

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March-Diaz, R., Lara-Ureña, N., Romero-Molina, C. et al. Hypoxia compromises the mitochondrial metabolism of Alzheimer’s disease microglia via HIF1. Nat Aging 1, 385–399 (2021). https://doi.org/10.1038/s43587-021-00054-2

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